Major Depressive Disorder (MDD) disrupts the lives of millions of people each year and presents a substantial societal and economic burden. Despite prior research, the mechanisms underlying treatment response and relapse in MDD remain unclear. Several treatments for MDD are available, but establishing the best form of treatment can be a protracted trial and error process where some patients remain unresponsive. The parent R01 for this proposed supplement is applying a leading-edge multimodal imaging approach to identify biomarkers indexing complementary aspects of treatment-induced brain plasticity focusing on fronto-limbic and striatal circuitry in an electroconvulsive therapy (ECT) treatment model. Magnetic resonance imaging (MRI) that includes structural, functional, diffusion and perfusion imaging and MR proton magnetic resonance spectroscopy (1HMRS) is being performed at 4 time points: prior to the 1st ECT treatment, after the 2nd ECT session, 1 week after completion of the ECT index series and at 6-months post treatment when relapse will be determined. Clinical assessments are being made at two additional interval time points. Demographically similar control subjects are being imaged twice to allow estimation of the variance associated with serial assessments and to determine normalization of biomarkers in association with treatment success in MDD. Leveraging the infrastructure of the R01, intramural funding has allowed us to also obtain blood samples at each of the imaging time points for two other biologically important measures complementary to the imaging and clinical data: peripheral lymphocyte gene expression levels and psychoneuroimmunology (PNI) measures of inflammation. Analysis of the preliminary gene expression data supports the potential for these measures, when used in concert with the imaging results, to more precisely characterize the neurobiological bases of MDD and the neural processes associated with treatment success. In this supplement, we propose to extend our neuroimaging biomarker aims to also include gene expression and PNI aims. As with parent R01, the potential impact of the proposed research to science and health is large. New scientific leads may inform novel treatment approaches, identify individuals at risk for developing depression, elucidate disease-related genomic or endophenotypic factors, identify subpopulations of MDD patients who are more likely to benefit from a particular treatment, and may predictively identify patients at high risk for relapse thereb allowing for the use of alternate or more aggressive individualized treatment strategies. Longitudinal measurements of gene expression and PNI markers, in combination with neuroimaging in the context of the rapid clinical response to ECT, is an innovative approach ideally suited for charting the trajectory of mental illness to determine where, when and how to intervene.